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      <title>OpenMed开源医疗AI模型 - 交互式测验</title>
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            <div class="quiz-header">
              <h1>OpenMed开源医疗AI模型 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
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  {
    "question": "根据文档，OpenMed项目旨在解决医疗AI领域的哪些核心痛点？",
    "options": [
      "提供一个完整的电子病历（EMR）管理系统",
      "解决商业模型成本高昂、闭源不透明以及领域覆盖有限的问题",
      "建立一个专为大型制药公司服务的私有AI模型市场",
      "开发一套用于医学影像（如X光、CT）自动诊断的软件"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档明确指出，OpenMed旨在解决医疗AI因高昂许可费、闭源模型不透明及领域覆盖有限而对中小团队和研究机构造成的创新障碍。它通过提供免费、开源的高质量NER模型来应对这些挑战。",
    "source": "文档中「它能解决什么问题」部分",
    "hint": "思考一下，文档开头提到了当前医疗AI面临的“高昂的许可费用、闭源模型的不透明性”等问题。"
  },
  {
    "question": "OpenMed提供的核心技术能力是什么？",
    "options": [
      "医学知识图谱的自动构建与推理",
      "生成式AI，用于撰写医疗研究论文",
      "命名实体识别（NER），用于从生物医学文本中提取关键信息",
      "一个用于管理和共享医疗数据集的云平台"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档的核心内容是围绕OpenMed提供超过380个医疗领域的命名实体识别（NER）模型展开的。其主要功能是从文本中精准地识别和提取如药物、疾病、基因等特定实体。",
    "source": "文档开篇介绍及「核心功能概述」部分",
    "hint": "文档第一句话就定义了OpenMed是“一个开源社区项目，提供超过380个医疗领域命名实体识别（NER）模型”。"
  },
  {
    "question": "相较于商业闭源模型，OpenMed的一个显著优势是什么？",
    "options": [
      "提供7x24小时的电话技术支持和SLA保障",
      "原生支持超过50种语言的医疗文本分析",
      "不仅免费开源，且在性能上据称超越了某些商业模型36%",
      "所有模型都极为轻量，可以在没有GPU的移动设备上运行"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档在“优势与特色”部分明确强调，OpenMed不仅免费、开源透明（基于Apache 2.0许可），而且性能优越，甚至超越商业模型36%。其他选项与文档描述相悖，如文档提到其对非英语支持有限，且部分模型需要较高GPU资源。",
    "source": "文档中「优势与特色」部分",
    "hint": "在“优势与特色”一节中，文档直接对比了OpenMed与闭源模型的性能差异。"
  },
  {
    "question": "一个开发者想要在自己的项目中使用OpenMed，需要满足哪些基本技术前提？",
    "options": [
      "必须拥有一台配置多张顶级GPU的服务器集群",
      "购买OpenMed的企业版许可证并获取API密钥",
      "安装Python 3.7+环境以及Hugging Face的Transformers库",
      "下载并编译OpenMed的C++源代码库"
    ],
    "correctAnswerIndex": 2,
    "explanation": "“上手指南”部分清晰地列出了入门步骤，第一步就是安装Python和相关的库（`pip install transformers datasets`）。这表明了使用OpenMed的基本技术门槛，而无需复杂的编译或昂贵的硬件/许可证。",
    "source": "文档中「上手指南」部分",
    "hint": "查阅“上手指南”的第一步，它描述了开始使用前需要执行的命令行操作。"
  },
  {
    "question": "关于OpenMed的使用成本和许可协议，以下哪项描述是正确的？",
    "options": [
      "项目完全免费，基于鼓励商业应用和修改的Apache 2.0许可证",
      "个人研究免费，但商业使用需要支付许可费用",
      "基于GPL许可证，任何使用其模型的项目也必须完全开源",
      "需要用户注册账户并同意数据共享协议后方可使用"
    ],
    "correctAnswerIndex": 0,
    "explanation": "文档在“使用门槛与成本”部分明确说明，OpenMed“完全免费，无需注册，基于Apache 2.0许可”。Apache 2.0是一种宽松的开源许可证，非常适合商业和学术应用。",
    "source": "文档中「使用门槛与成本」部分",
    "hint": "“使用门槛与成本”部分直接提到了项目的费用和许可证类型。"
  },
  {
    "question": "OpenMed在模型设计上如何满足不同用户的硬件需求？",
    "options": [
      "所有模型都统一为中等规模，以实现性能和资源的平衡",
      "只提供需要强大GPU支持的超大规模模型，以确保最高精度",
      "提供了从65M到568M等不同参数规模的模型，以适配多样化的硬件条件",
      "模型会自动根据用户的硬件配置动态调整其内部结构"
    ],
    "correctAnswerIndex": 2,
    "explanation": "“核心功能概述”中提到了OpenMed的一大特点是“多样模型规模”，并给出了具体的参数范围（从65M到568M）。这表明项目设计者考虑到了不同用户可能拥有从普通CPU到高端GPU等不同的计算资源，并为此提供了多种选择。",
    "source": "文档中「核心功能概述」部分",
    "hint": "核心功能列表中的第二点提到了“多样模型规模”。"
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  {
    "question": "以下哪个场景最能体现OpenMed的典型应用价值？",
    "options": [
      "为医院开发一套全新的在线挂号和支付系统",
      "从海量医学文献中自动抽取出所有提及的“基因”和“化学物质”实体，以加速药物研发",
      "实时监测ICU病房中病人的生命体征数据并进行预警",
      "为医学生创建一个用于模拟手术操作的3D虚拟现实应用"
    ],
    "correctAnswerIndex": 1,
    "explanation": "文档的“使用场景”部分列举了多个应用实例，包括“药物研究：识别文献中的化学物质和基因”。这正是一个典型的利用命名实体识别（NER）技术从非结构化文本中提取结构化信息的应用，完全符合OpenMed的核心功能。",
    "source": "文档中「使用场景」部分",
    "hint": "“使用场景”部分描述了如何利用OpenMed进行药物研究和癌症研究。"
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            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  